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Deep Learning for Facial Expression Recognition using EfficientNetB0

Deep Learning for Facial Expression Recognition using Effici...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Bhuvaneswari, R. Krishna, Chetlapalli Amritha Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9798350308167

In this study, we present a novel deep learning-based program for Facial Expression Recognition (FER) using the EfficientNetB0 architecture. The program is designed to automatically recognize and classify facial expressions in images, an essential component in fields like human-computer interaction and emotional analysis. We employ two distinct image data stores for training and testing, both sourced from the CK+48 dataset, while also augmenting the data for improved model generalization. An image input layer, a pruned EfficientNetB0 network, an entirely interconnected layer for categorization, a Softmax layer, and a layer for classification make up out model structure. The efficiency of the model is assessed using a different test dataset after it has been developed via random gradient descent with momentum. Notably, we assess model performance using important parameters like accuracy, precision, recall, and the F1 score. The importance of this program rests in its potential to improve human-machine communication by training computers to recognize and react to emotional cues in humans. Its application spans a wide range of domains, including healthcare, marketing, and entertainment, where understanding user emotions is of paramount importance. The results obtained from this FER model can empower applications like virtual assistants and affective computing systems to provide more personalized and context-aware responses. The developed application's robust architecture, training methodology, and evaluation metrics make it a valuable contribution to the area of FER. © 2023 IEEE.

关键词： Affective Computing EfficientNetB0 Emotion Analysis Expression recognition Human computer Interaction

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Urban Parking Management through Federated Learning: A Privacy-Preserving Approach to Parking Slot Prediction 2

Urban Parking Management through Federated Learning: A Priva...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Sha, Akhbar John, Jithin Varundev, M. Surendran, Sidharth Abhishek, S. Anjali, T. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350340235

In the contemporary urban landscape, the efficient management of parking resources has emerged as a critical concern. The ability to predict parking slot availability has far-reaching implications for urban mobility, traffic congestion mitigation, economic efficiency, and overall quality of life. This research paper presents a comprehensive approach that leverages deep learning and machine learning techniques, including neural network models, to address the multifaceted problem of parking slot prediction. Unique to this research is the integration of federated learning, a distributed approach that preserves data privacy and security while aggregating knowledge from spatial and temporal data sources. The research unfolds in two primary stages: spatial and temporal. In the spatial case, models are trained to understand the intricate relationships between parking slot occupancy and the physical characteristics of the environment. Simultaneously, in the temporal case, models capture temporal patterns in parking slot occupancy, considering variables such as time of day and seasonal *** aggregation combines the models’ insights, uniting the knowledge acquired from these diverse data sources. The result is a final model that harmonizes spatial and temporal understanding, while respecting data privacy and maintaining data security. This innovative approach results in a unified model that provides a nuanced and comprehensive understanding of parking slot prediction, with federated learning techniques safeguarding data integrity. © 2023 IEEE.

关键词： Deep Learning Federated Aggregation Federated Learning Neural Network Spatial Regression Time Series Analysis Traffic Congestion Vehicular Traffic

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Eliminating Control Plane Overload via Measurement Task Placement

Eliminating Control Plane Overload via Measurement Task Plac...

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作者： Chen, Xiang Liu, Hongyan Zhang, Dong Huang, Qun Zhou, Haifeng Wu, Chunming Yang, Qiang College of Computer Science and Technology Zhejiang University Hangzhou310007 China College of Computer Science and Big Data Fuzhou University Fuzhou350116 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China College of Control Science and Engineering Zhejiang University Hangzhou310007 China College of Electrical Engineering Zhejiang University Hangzhou310007 China

Recent efforts in network measurement place measurement tasks on programmable switches to measure high-speed traffic. These tasks extract flow data, i.e., events, from packets and send events to the control plane. However, the tasks may generate massive events in a short time. In this context, the links transferring events to the control plane and the control plane servers that handle events may be overloaded, i.e., control plane overload. None of existing solutions can eliminate control plane overload. In this paper, we propose MTP, a framework that eliminates control plane overload via careful measurement task placement. Our key idea is to allocate enough resources for each task during task placement to avoid control plane overload at runtime. For each task, MTP estimates its maximum possible rate of sending events to the control plane. Then its optimization framework addresses the resource restrictions of both switches and the control plane. The experiments on Tofino switches indicate that MTP outperforms existing solutions with higher accuracy in several use cases. © 2022 IEEE.

关键词： Job analysis

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User impersonation detection and authentication using image based OTP

User impersonation detection and authentication using image ...

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Raja Rajendran, A. Gokul Raj, J. Sharath Kumar, K. Mukeshvar, A. Radhika, G. Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350325188

In this age of the internet and technology, it is very important to be vigilant with one's devices and have safe and secure passwords to avoid several types of crimes. In this paper, we concentrate on two types of attacks: user impersonation and shoulder surfing. User impersonation is the act where one person pretends to be you or someone else to create confusion or obtain information that is private to you. To prevent this, we discuss a method to classify a person as authorised or unauthorised, utilising data from the user's mobile device's accelerometer and gyroscope sensors [1]. One of the simplest methods scammers use to acquire your personal information is by shoulder surfing, which may be as easy as simply looking over your shoulder. It is extremely common and hard to prevent in crowded areas. We proposed a method of authentication that uses OTPs and graphical password techniques and would prove effective against shoulder surfing attacks. © 2023 IEEE.

关键词： Accelerometer graphical password Gyroscope Impersonation KNN OTP Shoulder surfing

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CogniVision: Deep Learning Approaches for Image Recognition and Classification 2

CogniVision: Deep Learning Approaches for Image Recognition ...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Rajendran, Adithya Abhishek, S. Krishnan, Adarsh Ramesh, Nived Krishnan Anjali, T. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350340235

Image classification and interpretation pose significant challenges in the field of artificial intelligence (AI), with the rapid growth of technology and the availability of vast image datasets offering numerous opportunities for advancement. Transfer learning, a subset of machine learning, is widely applied in addressing these challenges. While existing machine learning models have shown impressive performance in image interpretation and scene classification, there are still hurdles to overcome. In many cases, relying solely on the weights of data-dependent models is insufficient. used model for image classification demonstrates rapid convergence. This study employs two distinct deep learning models for prediction: Inception V3, which achieves a Training Accuracy of 86% and Validation Accuracy of 88%, and ResNet50, trained for 19 epochs, reaching a maximum accuracy of 94.26%. This research addresses the strengths and weakness of current models and uses the potential of transfer learning to further advance the field of image classification and interpretation. © 2023 IEEE.

关键词： Image Classification Inception V3 ResNet50 Transfer learning

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Text Summarization and Image Decomposition of Medical Documents 4

Text Summarization and Image Decomposition of Medical Docume...

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4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Ramachandran, Raji Aggarwal, Arpit Varshney, Kanak Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350305258

Nowadays, the amount of data being generated in various fields, including the medical domain, has reached unprecedented levels. Consequently, the storage requirements have also increased significantly. Furthermore, accessing relevant information in emergency situations becomes a daunting task amidst such vast volumes of data. This challenge is particularly critical in medical field where a substantial amount of data is derived from scanning reports, encompassing both textual and image *** this context, we present a methodology aimed at addressing the storage needs of medical imaging outputs in a more efficient manner. Our proposed approach not only reduces the storage requirements but also ensures that the data is stored in a compact format, making it readily available for emergency situations. The efficacy of this methodology has been demonstrated through a series of experiments, yielding promising results. © 2023 IEEE.

关键词： BERT CUR Decomposition Medical etc Summary SVD

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Social Media Text to Formal Representation: A Knowledge Graph Approach 9

Social Media Text to Formal Representation: A Knowledge Grap...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Kommineni, Yaswanth Phani Somanchi, Srilekha Pavan Kumar, Boina Sai Ladani, Yash Nair, Lekshmi S Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350397376

Social media is an online platform where users freely share their views on various topics. By sharing their views, they come to a mutual agreement or disagreement with other users' views. Users are quite active on social media and the number of users is growing exponentially with time. This leads to an abundance of data available through social media. This data helps us in curating different research problems. One such research problem can be finding similarities between users by analyzing user data. data on social media is available in many forms like textual posts, comments, likes, shares, etc. Users often tend to express their diversity of opinions widely through comments on social media. Many opinionated and active users on social media are found in the comments section under a post. Hence, comments play a major role in determining similarity in user judgment about a topic. Likewise, people liking a post is also an essential feature in finding similarities between users. People with similar opinions tend to like posts of a specific type. This paper talks about how comments, textual posts, and likes for comments can be combined to form something called a keyword or a tag. These tags that are extracted from user data are further used in building a tag network. The tag network is used to make communities of users with similar interests. User grouping can also be done based on these extracted tags and comments under the posts. The proposed methods in this paper to achieve the above-mentioned are TF-IDF (Term frequency-inverse document frequency) followed by the TextRank algorithm. © 2023 IEEE.

关键词： Social networking (online)

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An Optimized Reinforcement Learning-Based Routing Protocol Using Spider Monkey Optimization Algorithm 2

An Optimized Reinforcement Learning-Based Routing Protocol U...

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2nd International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2023

作者： Radhika, G. Radhika, N. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

In Wireless Sensor Networks, numerous machine-learning algorithms are used in the packet routing strategy. The recommended routing approach is implemented using adaptive reinforcement learning and a spider monkey optimization algorithm. data packets are sent over the network via optimum route establishment - the above attempts to save energy by changing the Q-value at each instance. Technology enables adaptive mechanisms to demonstrate intelligent behavior in complex and dynamic situations such as Wireless Sensor Networks (WSN). Recently, new routing methods for WSNs have been devised in response to such a notion. Due to user interference and overall network performance, the numerous routing pathways cause frequent channel-switching occurrences. The spider monkey optimization method obtains routing metrics such as bandwidth, end-to-end latency, throughput, overhead, and packet delivery ratio. Compared to other machine learning algorithms, such as RLBR, the performance of WSN is improved and obtained better performance results. © Published under licence by IOP Publishing Ltd.

关键词： Wireless sensor networks

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A Multi-Objective Hyper-Heuristic Improved Configuration of Svm Based on Particle Swarm Optimization for Big data Cyber Security 3

A Multi-Objective Hyper-Heuristic Improved Configuration of ...

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3rd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Shekhawat, Jyoti Tajpal Vivekananda Global University Department of Computer Science Engineering Jaipur India Chandigarh School of Business Department of Master of Computer Application Jhanjeri India

ISBN: (纸本)9798350399264

Big data Cyber security analytics is a growingly significant field of study and application that uses big data techniques and technology to analyses security event data in order to safeguard networks, computers, and data from illegal access. There have been several reports on Big data Cybersecurity Analytic Systems in the literature, but an exhaustive and systematic analysis of the research from an architectural viewpoint is lacking. When In this paper, the SVM design process is organized as a bi-objective optimization problem with accuracy and model complexity as two competing goals. The system presents a novel, problem-independent hyper-heuristic framework for bi-objective optimization. It is novel to create a hyper heuristic for this situation. The hyper-heuristic structure proposed comprises of low-level heuristics and a significant level system. In light of the pursuit execution, the significant level system figures out which low-level heuristic ought to be utilized to create another SVM arrangement. Every one of the low-level heuristics look through the SVM design search space really utilizing an alternate arrangement of rules. To estimate the Pareto set of SVM arrangements, the suggested design adaptively combines the benefits of degradation with Pareto-based strategies for dealing with bi-objective enhancement. Microsoft's anomalous intrusion detection and big data malware classification have been used to gauge the efficiency of the suggested methodology. © 2023 IEEE.

关键词： Cybersecurity

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Studying the Effect of an Online Realm Based on Wearable Technology on Learners' Understanding

Studying the Effect of an Online Realm Based on Wearable Tec...

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Kirti Ali Khan, Mizan Chandigarh School of Business Department of Master of Computer Application India Sanskriti University Department of Computer Science Engineering Uttar Pradesh Mathura India

ISBN: (纸本)9798350322309

An interactive system called imperative (AR) creates a realistic and genuine educational and learning environment. The latest crop clearly prefers innovation training over conventional teaching methods. Studies on scientific test techniques are still lacking, despite the fact that earlier research on virtual reality has shown some information on how it might enhance learning results in several subjects. Students need experimental information to better understand the phenomenon. The page discusses the steps involved in constructing & implementing a mobile application for a lab experiment that makes use of augmented reality technology. An extra educational resource for high school seniors is this. The prime purpose of this mobile-based augmented reality program for a scientific experiment is to provide thorough information in addition to conventional processes and educational materials. The experiments that will be shown in ElectroAR include a number of them. An professional review is conducted to evaluate input. In the future, user testing of ElectroAR in actual classroom environments will be conducted. © 2023 IEEE.

关键词： Students

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